Starting with ASK: Rethinking Teaching, Learning and Assessment in the Age of AI

Introduction

Education today faces profound challenges and opportunities. Nowhere is this truer than in the education and skills sector, where the demand is not just for learners who can demonstrate knowledge, but for individuals who can adapt, apply, and respond to rapidly changing social and technological landscapes. Central to this challenge is a concept we use internally but which deserves wider recognition: ASK - Attitude, Skills, and Knowledge.

In my view, effective teaching, learning, and assessment must begin with ASK. If educators are serious about preparing learners for both work and life, they must look beyond knowledge alone and deliberately nurture skills and attitudes alongside it.

Why ASK Matters in the Education and Skills Sector

The education and skills sector has always had a dual mission: to prepare learners for academic success and to equip them for employment. Unlike purely academic disciplines, vocational and technical education cannot rely solely on the transmission of knowledge. A trainee electrician, for example, must not only know the theory of circuitry but must also demonstrate the skills to work safely with live systems and the attitude to follow regulations, work ethically, and collaborate effectively with colleagues.

Without a holistic focus on ASK, the risk is clear: learners may leave education holding certificates but lacking the full set of capabilities employers and communities require. Employers consistently report that soft skills, work ethic, and adaptability are as important, if not more important, than technical knowledge (CIPD, 2023). Starting with ASK ensures that these critical outcomes are defined, taught, and assessed intentionally.

Why ASK Matters in the Age of AI

The rise of artificial intelligence intensifies the need to foreground ASK. AI systems now perform many of the knowledge-based tasks that once defined academic and professional achievement. Generative AI can write essays, summarise research, solve equations, and even pass exams. If education continues to prioritise knowledge recall, it risks becoming irrelevant, outpaced by machines that can do this work faster and more accurately.

What AI finds more difficult to replicate, however, are the uniquely human aspects of learning:

• Attitudes such as empathy, resilience, curiosity, and ethical judgment.

• Skills such as critical thinking, collaboration, leadership, creativity, and communication.

These are precisely the dimensions that ASK brings to the fore. In this new era, education must shift from asking “what do learners know?” to “what can learners do with what they know, and what kind of people are they becoming?”

Designing Learning with ASK in Mind

Starting with ASK involves three key stages:

1. Defining outcomes: What attitudes, skills, and knowledge should learners hold at the end of a module or programme?

2. Developing strategies: If the sky were the limit, what would be the most effective ways to cultivate those outcomes? Only after imagining the best-case scenario do we adjust for practical realities like time, resources, and funding.

3. Designing assessments: How do we check that learners have truly acquired ASK? Again, we should begin with the most effective assessment methods, such as observation, performance tasks, or portfolios, before deciding what is feasible. Importantly, these assessments should not be confined to one-off, end-of-course events but embedded throughout the learning journey.

   
   

A Foundation in Bloom’s Taxonomy

ASK is underpinned by Bloom’s original taxonomy of educational objectives (Bloom, 1956), which outlined not one but three domains:

• Cognitive (knowledge)

• Affective (attitudes and values)

• Psychomotor (skills)

Yet, as education systems evolved, the cognitive domain became dominant, often to the neglect of the other two. This imbalance is particularly problematic today. As Anderson and Krathwohl (2001) argued, learning is most powerful when all domains are addressed. Hattie’s (2009) research similarly shows that outcomes improve when skills and attitudes are deliberately cultivated alongside knowledge.

Why Knowledge Alone is Insufficient

Focusing solely on knowledge produces narrow outcomes that no longer align with societal or labour market needs. In the context of AI, knowledge without skills or attitudes is not just insufficient, it is obsolete. What matters is the human capacity to use knowledge wisely, to adapt it creatively, and to apply it ethically in unpredictable contexts.

Ashford-Rowe, Herrington, and Brown (2014) call for authentic assessment, in which learners demonstrate integrated ASK in tasks that mirror real-world challenges. Such approaches ensure education remains relevant in a world where AI performs many traditional “academic” tasks.

Conclusion

Starting with ASK—Attitude, Skills, and Knowledge, offers a blueprint for rethinking education and training in the twenty-first century. In the skills sector, it ensures learners leave with more than just qualifications: they develop the full set of competencies needed for employment and citizenship. In the age of AI, it future-proofs education, safeguarding the uniquely human dimensions of learning that machines cannot replicate.

Education, therefore, must move beyond a narrow focus on what learners know and embrace what they can do and who they are becoming.

References

• Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman.

• Ashford-Rowe, K., Herrington, J., & Brown, C. (2014). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education, 39(2), 205–222.

• Bloom, B. S. (Ed.). (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay.

• Chartered Institute of Personnel and Development (CIPD). (2023). Employer views on skills and employment. London: CIPD.

• Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London: Routledge.